The field of exposomics is revolutionizing health science by unlocking the power of advanced technologies to study the complex interactions between environmental, social, and psychological factors that shape our biology. By analyzing the molecular fingerprints left in our bodies from every breath we take, every meal we eat, and every environment we encounter, researchers are uncovering new insights into disease prevention and personalized medicine.

Led by the Banbury Exposomics Consortium, an interdisciplinary group of scientists gathered at Cold Spring Harbor’s Banbury Center to define the core principles of this rapidly evolving field. Gary Miller, PhD, a foremost expert in exposomics and faculty member at Columbia University Mailman School of Public Health, was the lead organizer of the Consortium.

Exposomics explores how environmental factors such as pollutants in our water and food, social stressors, and psychological factors shape our biology. By studying these combined exposures, researchers can uncover how they collectively influence health, from metabolism and heart function to brain health and disease risk.

The young field is already proving its transformative potential. Researchers analyzing molecular evidence identified a specific industrial solvent as the culprit behind kidney disease clusters among factory workers. In another study, scientists merged satellite pollution mapping with residential location information to reveal how airborne particulates prematurely age the brain.

These discoveries are made possible by cutting-edge technologies and tools such as wearable sensors that track chemical exposures in real-time, satellite imagery that maps pollution down to city blocks, and ultra-sensitive mass spectrometers that detect compounds present at just one part per trillion.

While genetics provides our biological blueprint, it explains only a fraction of chronic disease risk. The exposome captures everything that happens to us, from industrial chemicals to social stressors. Unlike traditional studies examining single exposures in isolation, exposomics integrates advanced tools to understand how environmental, social, and psychological factors collectively interact with our biology.

Systematically analyzing these complex interactions can improve drug development, uncover hidden drivers of disease, and address health disparities. The approach bridges precision medicine and population health.

Miller and colleagues outline critical priorities for advancing exposomics, including the development of more sensitive technologies, creating a human exposome reference to enable analysis and contextualization at the population scale, and implementing standardized protocols to enable AI-driven analysis of complex datasets.

Newly launched U.S. and European exposomics hubs now provide the infrastructure for worldwide collaboration, standardizing methods, harmonizing data, and training researchers in the cross-disciplinary skills needed to advance this field.

“We’re now building the first systematic framework to measure how all exposures — from chemical to social — interact with biology across the lifespan,” says Miller. “Our goal is to create actionable strategies for healthier lives.”

The article discusses new research from the University of Massachusetts Amherst that shows upgrading electrical and broadband infrastructure using a “dig once” approach is nearly 40% more cost-effective than replacing them separately. The study found that co-undergrounding – burying both electric and broadband internet lines together – saves costs, making it feasible for smaller towns in Massachusetts to make undergrounding upgrades.

Using computational modeling across various infrastructure upgrade scenarios, the researchers found that co-undergrounding is 39% more cost-effective than separately burying electrical and broadband wires. They also explored how aggressively towns should pivot to putting lines underground, considering factors such as the cost of converting lines from above ground to underground, the cost of outages, and the hours of outages that can be avoided if lines are underground.

A case study in Shrewsbury, Massachusetts, found that an aggressive co-undergrounding strategy over 40 years would cost $45.4 million but save $55.1 million from avoiding outages, considering factors like spoiled food, damaged home appliances, missed remote work hours, and increased use of backup power sources.

The researchers also took into account additional benefits such as increased property values from the aesthetic improvement of eliminating overhead lines, resulting in a net benefit of $11.3 million. They concluded that aggressively converting just electrical wires to underground was less expensive but had a significantly lower net benefit than co-undergrounding.

Future research directions include quantifying the impacts of co-undergrounding across various geographic locations and scenarios, investigating alternative underground routing options, and other potential outage mitigation strategies.

The study’s findings aim to help decision makers prioritize strategic planning for infrastructure upgrades, considering factors like soil composition, network type, and land use variables. The ultimate goal is to encourage utilities and towns to think strategically about upgrading electrical and broadband infrastructure using a “dig once” approach.

The world is facing a growing health crisis – drug-resistant infections. These infections are not only harder to treat but also require more expensive and toxic medications, leading to longer hospital stays and higher mortality rates. In 2021 alone, 450,000 people developed multidrug-resistant tuberculosis (TB), with treatment success rates dropping to just 57%, according to the World Health Organization.

Tulane University scientists have developed a groundbreaking artificial intelligence-based method that more accurately detects genetic markers of antibiotic resistance in deadly bacteria like TB and staph. This innovative approach has the potential to lead to faster and more effective treatments.

The researchers introduced a new Group Association Model (GAM) that uses machine learning to identify genetic mutations tied to drug resistance. Unlike traditional tools, which can mistakenly link unrelated mutations to resistance, GAM doesn’t rely on prior knowledge of resistance mechanisms, making it more flexible and able to find previously unknown genetic changes.

Current methods of detecting resistance take too long or miss rare mutations. Tulane’s model addresses both problems by analyzing whole genome sequences and comparing groups of bacterial strains with different resistance patterns to find genetic changes that reliably indicate resistance to specific drugs. This is like using the bacteria’s entire genetic fingerprint to uncover what makes it immune to certain antibiotics.

In the study, the researchers applied GAM to over 7,000 strains of Mtb and nearly 4,000 strains of S. aureus, identifying key mutations linked to resistance. They found that GAM not only matched or exceeded the accuracy of the WHO’s resistance database but also drastically reduced false positives, wrongly identified markers of resistance which can lead to inappropriate treatment.

The model’s ability to detect resistance without needing expert-defined rules means it could potentially be applied to other bacteria or even in agriculture, where antibiotic resistance is also a concern in crops. This tool can help us stay ahead of ever-evolving drug-resistant infections and provide a clearer picture of which mutations actually cause resistance, reducing misdiagnoses and unnecessary changes to treatment.

When combined with machine learning, the ability to predict resistance with limited or incomplete data improved. In validation studies using clinical samples from China, the machine-learning enhanced model outperformed WHO-based methods in predicting resistance to key front-line antibiotics. Catching resistance early can help doctors tailor the right treatment regimen before the infection spreads or worsens.

It’s vital that we stay ahead of ever-evolving drug-resistant infections. This AI-powered diagnosis tool has the potential to revolutionize the way we detect and treat these deadly bacteria, leading to better patient outcomes and improved global health.